Title:  Tutorial on Deep Learning and Generative AI

Instructor: Haoda Fu (Eli Lilly and Company)

Abstract: Designed specifically for individuals possessing a strong foundation in statistics and biostatistics, this course seeks to bridge the gap into the realm of deep learning and generative AI. Beginning with fundamental knowledge of deep learning, participants will be guided through hands-on implementations using the PyTorch framework. As we delve deeper, the course will unpack popular architectures that have reshaped the landscape of artificial intelligence, including CNN, GNN, ResNet, U-net, attention mechanisms, and transformers. Given the increasing importance of AI in healthcare, special emphasis will be laid on techniques tailor-made for medical imagery and drug discovery, such as SE(3) equivariant machine learning. As a culmination, participants will be introduced to the various facets of generative AI, encompassing GANs, VAEs, DDPM, and score-based generative models. Whether you're seeking to apply these technologies in healthcare, research, or any other domain, this tutorial promises a comprehensive insight into the world of generative AI and deep learning.

Dr. Haoda Fu is an Associate Vice President and an Enterprise Lead for Machine Learning, Artificial Intelligence, and Digital Connected Care from Eli Lilly and Company. Dr. Haoda Fu is a Fellow of ASA (American Statistical Association), and IMS Fellow (Institute of Mathematical Statistics). He is also an adjunct professor of biostatistics department, Univ. of North Carolina Chapel Hill and Indiana university School of Medicine. Dr. Fu received his Ph.D. in statistics from University of Wisconsin - Madison in 2007 and joined Lilly after that. Since he joined Lilly, he is very active in statistics and data science methodology research. He has more than 100 publications in the areas, such as Bayesian adaptive design, survival analysis, recurrent event modeling, personalized medicine, indirect and mixed treatment comparison, joint modeling, Bayesian decision making, and rare events analysis. In recent years, his research area focuses on machine learning and artificial intelligence. His research has been published in various top journals including JASA, JRSS, Biometrika, Biometrics, ACM, IEEE, JAMA, Annals of Internal Medicine etc.. He has been teaching topics of machine learning and AI in large industry conferences including teaching this topic in FDA workshop. He was board of directors for statistics organizations and program chairs, committee chairs such as ICSA, ENAR, and ASA Biopharm session. He is a COPSS Snedecor Awards committee member from 2022-2026, and will also serve as an associate editor for JASA theory and method from 2023.

Title:  Generative Learning, Denoising Diffusions, and Large Models

Instructor: Jian Huang (The Hong Kong Polytechnic University)

Abstract:This short course focuses on statistical generative learning leveraging pretrained large models. The first part of the course will introduce the basics of two generative learning approaches: generative adversarial networks and denoising diffusion models. The second part of the course will use two examples to illustrate generative modeling with the help of a pretrained large model. The first example considers protein data generation based on data representations learned through a large model trained with a big protein sequence database. The second example demonstrates a Bayesian fine-tuning approach for image generation leveraging a large pretrained diffusion model.

Jian Huang is Chair Professor of Data Science and Analytics in the Department of Applied Mathematics at The Hong Kong Polytechnic University. He obtained his Ph.D. degree in Statistics from the University of Washington in Seattle. His research interests include semiparametric inference, nonparametric statistics, high-dimensional statistics, large sample theory, computational statistics, integrative analysis, survival analysis, statistical genetics, and bioinformatics. His more recent research interests include deep generative models and inference, statistical inference in deep learning, deep neural network approximation theory, representation learning, and statistical analysis leveraging pretrained large models. He has published widely in the fields of Statistics, Biostatistics, Machine Learning, Bioinformatics and Econometrics. He was designated a highly cited researcher in the field of Mathematics from 2015 to 2019 by the Web of Science group at Clarivate and included in the list of top 2% of the world's most cited scientists by Elsevier BV and Stanford University (2022, 2023). Professor Huang is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics.